仅用触觉数据，快速估计未知刚体的形状与位置。
Active Tactile Exploration for Rigid Body Pose and Shape Estimation
- 基于触觉反馈构建物理约束损失函数，避免数值刚性问题。
- 10秒内用随机接触数据即可学习立方体与凸多面体形状。
- 探索策略最大化信息增益，适合真实机器人快速感知任务。
通用机器人操作需处理未见过的物体。在测试时学习物理准确模型可显著提升数据效率、预测性和任务复用性。触觉传感对遮挡具有鲁棒性，但其时间稀疏性要求谨慎的在线探索以维持数据效率。直接接触可能导致物体移动，需同时估计形状与位置。本文提出一种仅依赖触觉数据的学习与探索框架，能以最少的机器人运动同时确定刚体的形状与位置。我们基于近期接触丰富的系统辨识进展，设计了一种惩罚物理约束违反的损失函数，不引入刚体接触中的数值刚性。优化该损失后，可在首次接触后不到10秒内，利用随机采集的数据学习立方体和凸多面体几何。我们的探索策略旨在最大化期望信息增益，在模拟与真实机器人实验中均实现显著更快的学习速度。更多信息请访问 https://dairlab.github.io/activetactile。
原文摘要 · Abstract (English)
General robot manipulation requires the handling of previously unseen objects. Learning a physically accurate model at test time can provide significant benefits in data efficiency, predictability, and reuse between tasks. Tactile sensing can compliment vision with its robustness to occlusion, but its temporal sparsity necessitates careful online exploration to maintain data efficiency. Direct contact can also cause an unrestrained object to move, requiring both shape and location estimation. In this work, we propose a learning and exploration framework that uses only tactile data to simultaneously determine the shape and location of rigid objects with minimal robot motion. We build on recent advances in contact-rich system identification to formulate a loss function that penalizes physical constraint violation without introducing the numerical stiffness inherent in rigid-body contact. Optimizing this loss, we can learn cuboid and convex polyhedral geometries with less than 10s of randomly collected data after first contact. Our exploration scheme seeks to maximize Expected Information Gain and results in significantly faster learning in both simulated and real-robot experiments. More information can be found at https://dairlab.github.io/activetactile
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